Publications
2024
- Harihara Subrahmaniam Muralidharan, Noam. Y Fox, and Mihai PopFrontiers in Microbiology 2024
A common task in the analysis of microbial communities involves assigning taxonomic labels to the sequences derived from organisms found in the communities. Frequently, such labels are assigned using machine learning algorithms that are trained to recognize individual taxonomic groups based on training data sets that comprise sequences with known taxonomic labels. Ideally, the training data should rely on labels that are experimentally verified-formal taxonomic labels require knowledge of physical and biochemical properties of organisms that cannot be directly inferred from sequence alone. However, the labels associated with sequences in biological databases are most commonly computational predictions which themselves may rely on computationally-generated data-a process commonly referred to as "transitive annotation". In this manuscript we explore the implications of training a machine learning classifier (the Ribosomal Database Project’s Bayesian classifier in our case) on data that itself has been computationally generated. We demonstrate that even a few computationally-generated training data points can significantly skew the output of the classifier to the point where entire regions of the taxonomic space can be disturbed. We also discuss key factors that affect the resilience of classifiers to transitively-annotated training data, and propose best practices to avoid the artifacts described in our paper.
2023
- Tu Luan, Harihara Subrahmaniam Muralidharan, Marwan Alshehri, Ipsa Mittra, and Mihai PopNucleic Acids Research 2023
16S rRNA gene sequence clustering is an important tool in characterizing the diversity of microbial communities. As 16S rRNA gene data sets are growing in size, existing sequence clustering algorithms increasingly become an analytical bottleneck. Part of this bottleneck is due to the substantial computational cost expended on small clusters and singleton sequences. We propose an iterative sampling-based 16S rRNA gene sequence clustering approach that targets the largest clusters in the data set, allowing users to stop the clustering process when sufficient clusters are available for the specific analysis being targeted. We describe a probabilistic analysis of the iterative clustering process that supports the intuition that the clustering process identifies the larger clusters in the data set first. Using real data sets of 16S rRNA gene sequences, we show that the iterative algorithm, coupled with an adaptive sampling process and a mode-shifting strategy for identifying cluster representatives, substantially speeds up the clustering process while being effective at capturing the large clusters in the data set. The experiments also show that SCRAPT (Sample, Cluster, Recruit, AdaPt and iTerate) is able to produce operational taxonomic units that are less fragmented than popular tools: UCLUST, CD-HIT and DNACLUST. The algorithm is implemented in the open-source package SCRAPT. The source code used to generate the results presented in this paper is available at https://github.com/hsmurali/SCRAPT.
- Gabriel Birzu, Harihara Subrahmaniam Muralidharan, Danielle Goudeau, Rex R. Malmstrom, Daniel S. Fisher, and Devaki BhayaeLife 2023
Bacterial species often undergo rampant recombination yet maintain cohesive genomic identity. Ecological differences can generate recombination barriers between species and sustain genomic clusters in the short term. But can these forces prevent genomic mixing during long-term coevolution? Cyanobacteria in Yellowstone hot springs comprise several diverse species that have coevolved for hundreds of thousands of years, providing a rare natural experiment. By analyzing more than 300 single-cell genomes, we show that despite each species forming a distinct genomic cluster, much of the diversity within species is the result of hybridization driven by selection, which has mixed their ancestral genotypes. This widespread mixing is contrary to the prevailing view that ecological barriers can maintain cohesive bacterial species and highlights the importance of hybridization as a source of genomic diversity.
2021
- Harihara Subrahmaniam Muralidharan, Nidhi Shah, Jacquelyn S. Meisel, and Mihai PopFrontiers in Microbiology 2021
High-throughput sequencing has revolutionized the field of microbiology, however, reconstructing complete genomes of organisms from whole metagenomic shotgun sequencing data remains a challenge. Recovered genomes are often highly fragmented, due to uneven abundances of organisms, repeats within and across genomes, sequencing errors, and strain-level variation. To address the fragmented nature of metagenomic assemblies, scientists rely on a process called binning, which clusters together contigs inferred to originate from the same organism. Existing binning algorithms use oligonucleotide frequencies and contig abundance (coverage) within and across samples to group together contigs from the same organism. However, these algorithms often miss short contigs and contigs from regions with unusual coverage or DNA composition characteristics, such as mobile elements. Here, we propose that information from assembly graphs can assist current strategies for metagenomic binning. We use MetaCarvel, a metagenomic scaffolding tool, to construct assembly graphs where contigs are nodes and edges are inferred based on paired-end reads. We developed a tool, Binnacle, that extracts information from the assembly graphs and clusters scaffolds into comprehensive bins. Binnacle also provides wrapper scripts to integrate with existing binning methods. The Binnacle pipeline can be found on GitHub (
https://github.com/marbl/binnacle ). We show that binning graph-based scaffolds, rather than contigs, improves the contiguity and quality of the resulting bins, and captures a broader set of the genes of the organisms being reconstructed.
2019
- Srinarayana Nagarathinam, Harihara Subrahmaniam Muralidharan, Arunchandar Vasan, Venkatesh Sarangan, Sermisha Narayana, and Anand SivasubramaniamIn Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation 2019
During demand response events, utility customers typically make decisions to reduce their own load independent of other customers. In the presence of district cooling with a centralized cooling plant, the control decisions of buildings and the district cooling plant become coupled, since the setpoints chosen by the cooling plant affect the consumption of the buildings and vice versa.While past works on demand response address control of building-level heating and cooling systems, control of such systems in the presence of district cooling has not received much attention. We consider the problem of minimizing the discomfort of buildings while meeting the target demand reduction. Specifically, we identify the optimal setpoints for the buildings and district cooling plant, even while obeying the non-linear, coupled thermodynamic constraints between the district cooling plant and buildings. We propose a novel solution strategy using domain knowledge that transforms the complex non-linear optimization problem to a series of quadratic programming which can be then solved conventionally.We validate the performance of the proposed strategy by comparing it with a combinatorial brute-force solution on a small data set. We also evaluate the performance of our strategy on a real-world data set of 416 buildings that are served by a district cooling plant. The results indicate that including the district plant in demand response and solving with the coupled constraints, allows the utility to meet higher target reductions for the same comfort levels. Also, the proposed solution strategy is both fast (at least 4x) and scalable (35x) when compared with conventional optimization solvers.